Due to their favorable electrical and optical properties, quantum dots (QDs) nanoparticles have found numerous applications including nanomedicine. However, there have been concerns about their potential environmental impacts. The objective of this study is to develop an agent-based simulation model for predicting the diffusion dynamics and concentration of toxic materials released from QDs. Reaction kinetics is used to model the stability of surface capping agent particularly due to oxidation process.

This paper first presents a simulation model implemented to study a specific workcenter in semiconductor manufacturing facilities (fabs) with the objective of controlling the risk on process equipment. The different components of the model, its inputs and its outputs, that led us to propose improvements in the workcenter, are explained. The risk evaluated in this study is the exposure level in the number of wafers on a process tool since the latest control performed for this tool, based on an indicator called Wafer at Risk. Our analysis shows that measures should be better managed to avoid lack of control and that an appropriate qualification strategy is required.

A virtual factory should represent most of the features and operations of the corresponding real factory. Some of the key features of the virtual factory include the ability to assess performance at multiple resolutions and generate analytics data similar to that possible in a real factory. One should be able to look at the overall factory performance and be able to drill down to a machine and analyze its performance. It will require a large amount of effort and expertise to build such a virtual factory. This paper describes an effort to build a multiple resolution model of a manufacturing cell. The model provides the ability to study the performance at the cell level or at the machine level. The benefits and limitations of the presented approach and future research directions are also described.

Until relatively recently, developing hybrid simulation models using more than one simulation paradigm was a challenging task which required a degree of ingenuity on behalf of the modeler. Generally speaking, such hybrid models either had to be coded from scratch in a programming language, or developed using two (or more) different off-the-shelf software tools which had to communicate with each other through a user-written interface. Nowadays a number of simulation tools are available which aim to make this task easier. This paper does not set out to be a formal review of such software, but it discusses the increasing popularity of hybrid simulation and the rapidly developing market in hybrid modeling tools, focusing specifically on applications in health and social care and using experience from the Care Life Cycle project and elsewhere.

The advantages of combined simulation techniques have been already frequently discussed and are well-covered by the recently published literature. In particular, many case studies have been presented solving similar domain-specific problems by different multi-paradigm simulation approaches. Moreover, a number of papers exist focusing on theoretical and conceptual aspects of hybrid simulation. However, it still remains a challenge to decide, whether combined methods are appropriate in certain situations and how they can be applied. Therefore, domain-specific user guides for multi-paradigm modeling are required combining general concepts and best practices to common steps. In this paper, we particularly outline three major processes targeting to define structured hybrid approaches in domain-specific contexts, and we focus on some practical issues aiming to a sustainable model development. Finally, an example hybrid methodology for problems in healthcare will be presented.

The weather is unpredictable and can have a large impact on the profitability of seasonal businesses, particularly if staffing requirements are highly temperature-dependent. In this paper we describe our efforts in developing a what-if analysis tool to assist affected Small and Medium Enterprises in determining the best case scenario for timing hiring new staff and deciding the optimum length of temporary employment contracts.

This work simulated some alternatives of dynamic allocation of additional human resources in a company that produces various products from Pupunha palm. Its goal was to increase the average amount of trays produced per day in this line through a hybrid application of discrete event and agent-based simulation. Two different decision-making forms were proposed to find out which workstation should have received an additional operator. The first proposal was made on the level of occupancy of the operators, while the second one was made on the queue size. The computational model was operationally validated by comparing its results with the actual production data of the company.

A tunnel boring machine (TBM) is the primary resource in a tunnel construction project and generally its advance rate is equal to the performance rate of the whole project. Regarding previous studies, the utilization factor of TBMs is approximately 50% most of the time. The process of repair and maintenance of various parts of the machine and the logistic equipment takes 50% of the time. This case study aims to simulate the whole process of TBM tunneling in Ahwas subway project and find out how different scenarios of repair and maintenance can affect the utilization factor of the TBM. The model is developed using discrete-event simulation (DES) method.

The operation of offshore drilling platforms requires a lot of logistics: supply of platforms by platform supply vessels (PSVs), backward transportation of waste in containers and transportation of oil by tankers to export ports. The severe weather conditions of the Arctic Ocean increase the number of possible disruptions that influence the logistic system. The operation of PSVs and tankers has multiple constraints and interactions. An agent-based simulation has been developed in AnyLogic to support the strategic planning of logistics by year 2042. The presentation discusses the use of the model to determine the required number of vessels and compare different options of crude oil outbound logistic network design.

In collaboration with a Midwest Utility Provider, we developed a cyber defense econometric model via Anylogic that not only simulates the operational process of the Utility's local distribution infrastructure, but also helps to minimize the cost of implementing security. By measuring the economic impact of various cyber attacks affecting disparate components of the distribution infrastructure, it was discovered that both extremes of the paradigm (no security measures implemented vs. securing every device) were unacceptable solutions in regards to protecting the business financially.